Performance Evaluation and Lockdown Decisions of the UK Healthcare System in Dealing with COVID-19: a Novel Unbiased MCDM Score Decomposition into Latent Vagueness and Randomness Components
DOI:
https://doi.org/10.31181/dmame7120241041Keywords:
Lockdown drivers, Multiple-Criteria Decision Making, Latent Vagueness and Randomness Components, COVID-19, Unbiased scoresAbstract
This paper examines the performance evaluation and lockdown drivers of the UK's NHS (National Health Service) during the COVID-19 pandemic. The study aims to enhance the NHS's response to future health crises and guide government lockdown decisions. Lockdown drivers encompass vital resources like beds, ventilators, patients, and staff. A three-stage MCDM (Multiple-Criteria Decision Making) approach is employed to analyze performance scores. First, partial utility functions or partial distances are computed using COPRAS (Complex Proportional Assessment) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), respectively. Second, the Latent Vagueness and Randomness Components (LAVRA) method filters unbiased performance scores from uncertain components. Third, a bootstrapped neural network regression classifies lockdown drivers based on performance, deaths, and geographic regions. Crucial drivers relate to ventilated bed availability, while less critical ones include staff absence due to COVID-19 and a high admission rate of elderly inpatients. The results indicate performance scores range from 0.65 to 0.75 using TOPSIS, while COPRAS analysis significantly reduces the scores. Lockdown decisions are influenced by geographic regions, death tolls, and unbiased hospital performance scores.
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